Mitocondrial pyruvate metabolism

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Mitocondrial pyruvate metabolism(MPM) is a pseudo reaction that represents the total ATP production from one unit of pyruvate in the mitochondrian.

Chemical reaction

 Pyruvate  + 13ADP + 13Pi \leftrightarrow 13ATP

Rate equation

  • Chemical reactions proceed to equilibrium within closed systems. For a simple reaction A \rightarrow B it is defined as K_{eq} = \frac{[B]_{eq}}{[A]_{eq}} where forward and reverse rates are equal.
  • Equilibrium is not reached in open system due to influx and outflux. Mass action ratio[1] \tau for A \rightarrow B reaction is defined as \tau = \frac{[B]_{ob}}{[A]_{ob}} where subscript ob represents observable at a given point.
  • Deviation from equilibrium is measured with Disequilibrium constant \rho as \rho = \frac{\tau}{K_{eq}}
  • Given the simple uni molecular reaction A \leftrightarrow B the mass action equation can be modified as
v=K_1A-K_2B

 v=K_1A \left(1-\frac{K_2B}{K_1A} \right)

Considering K_{eq} = \frac{K_1}{K_2} and \tau = \frac{P_1P_2 \ldots}{S_1S_2 \ldots} we have,

 v=K_1A \left(1-\frac{\tau}{K_{eq}} \right)

  • The generalized arbitrary mass action ratio gives us
 v = K_1A^{n_1}B^{n_2} \ldots \left(1-\frac{\tau}{K_{eq}} \right)
Or,

 v = K_1A^{n_1}B^{n_2} \ldots (1 - \rho)

For eg. for the reaction 2A + B \leftrightarrow C + 2D, the rate law would be K_1A^2B \left(1-\rho\right)

  • This equation demonstrates how a rate expression can be divided into parts that include both kinetics and thermodynamic properties [2].
  • Given the net rate of reaction  v = v_f \left( 1 - \frac{v_r}{v_f} \right), we have
 v = v_f \left(1 - \rho \right)

In this model

  • The rate law is defined as
    v = K_1[Pyruvate][ADP]^{13}[Pi]^{13}\left(1-\frac{\frac{[ATP]^{13}}{[Pyruvate][ADP]^{13}[Pi]^{13}}}{K_{eq}}\right)

  • The overall standard free-energy change for Pyruvate metabolism is \Delta G^o{'}= -50.3 Kj/Mol[3][4].
Calculating K_{eq} value from these free energy gives \Delta G' = - 50.3\ kJ.mol^{-1}, Failed to parse (Cannot store math image on filesystem.): Keq = exp(-\frac{\Delta G'}{RT}) = exp(\frac{50300}{8.31*298.15}) \approx 654904512.15 . In order to ensure that the uncertainty does not affect the model equilibrium a small uncertainty of 5% can be considered for transporter. In our model we have applied this approach. So Failed to parse (Cannot store math image on filesystem.): K_{eq} = 654904512.15 \pm 32745226 .
  • The Flux of pyruvate consumed by mitochondria measured for AS_30D is  v = 1.8 [5].
  • The steady state concentrations for substrates and products are ATP=8.7 \pm 3 (5), ADP = 2.7 \pm 1.3, Pyruvate = 8.5 \pm 3.6 and Pi = 7.5.
  • The K_1 value calculated from the above mentioned values are Failed to parse (Cannot store math image on filesystem.): 7.78E-015
  • To calculate the uncertainty of K_1 we first looked at the uncertainty on the substrate and product concentration. The maximum uncertainty reported for these values are \approx 50%. In our model we considered this 50% uncertainty in its mean value giving value of 7.78E^{-015} \pm 3.89E^{-015}

Parameter values

Parameter Value Organism Remarks
K_1 Failed to parse (Cannot store math image on filesystem.): 2.20E^{-018}
K_{eq} Failed to parse (Cannot store math image on filesystem.): 221941.39

Parameters with uncertainty

Parameter Value Organism Remarks
K_1 Failed to parse (Cannot store math image on filesystem.): 2.20E^{-018} \pm 1.099E^{-018}
K_{eq} Failed to parse (Cannot store math image on filesystem.): 221941.39


References

  1. Hess B. and Brand K. (1965), Enzymes and metabolite profiles. In Control of energy metabolism. III. Ed. B. Chance, R. K. Estabrook and J. R. Williamson. New York: Academic Press
  2. Sauro H M, Enzyme Kinetics for Systems Biology, Second Edition, Ambrosius Publishing (2013), ISBN-10: 0-9824773-3-3
  3. Nelson D. and Cox M. (2008), Lehninger Principles of Biochemistry, Fight Edition, W.H. Freeman and Company, ISBN-10: 071677108X
  4. http://crystal.res.ku.edu/taksnotes/Biol_638/notes/chp_16.pdf
  5. Marín-Hernández A, Gallardo-Pérez JC, Rodríguez-Enríquez S et al (2011) Modeling cancer glycolysis. Biochim Biophys Acta 1807:755–767 (doi)